Basic Imaging Processing and machine learning in Radiology Workshop

Open source software has had an expanding role in the development of image analysis routines for radiology research applications. Compared to commercial proprietary software, open source software is often more adaptable and economical. The purpose of this workshop is to familiarize participants with the features of ImageJ/FIJI , an open source image processing program The utility of ImageJ for performing automated image filtration, segmentation and registration tasks that are repeatable and can be tailored using information on the DICOM headers of images will be discussed. Case studies using clinical images will be used to demonstrate the capabilities of ImageJ. These demonstrations will also show how the capabilities of this software can be expanded using macros scripts and plug-ins (FIJI only). A quick demonstration of WEKA (https://www.cs.waikato.ac.nz/ml/weka/ ) a machine learning package embedded in FIJI.

DETAILS

March 16, 2019 at 14:00-15:00 (WORKSHOP ROOM)

PREREQUISITES

  • Bring your own laptop and download ImageJ or FIJI (Fiji Is Just ImageJ with extra plugins)
  • https://imagej.nih.gov/ij/download.html (for image processing only)
  • http://fiji.sc (for image processing and machine learning)

TARGET AUDIENCE

  • Radiographer
  • Radiologist
  • Medical Physicist
  • Quality Officer
  • Scientist Researcher

WORKSHOP FACULTY

Metab Al Kubeyyer Image

Metab Alkubeyyer, MD
Consultant Body MRI and Imaging Informaticist
King Khalid University Hospital, Riyadh , Saudi Arabia
King Saud University Medical City, Riyadh , Saudi Arabia

LEARNING OBJECTIVES

Upon completion of this workshop, attendees should be able to:

 

  1. Open DICOM images and check DICOM tags
  2. Apply filters
  3. Enhance Image contrast and apply basic filters.
  4. Extract some imaging features.
  5. Apply machine learning algorithm for segmentation.

HOW TO REGISTER?

Registration can be done onsite. LIMITED SEATS AVAILABLE ONLY!